With their ever-increasing performance and lowering cost, many robots (e.g., machines configured to automatically/autonomously execute physical actions) are now extensively used in many fields. Robots, for example, can be used to execute various tasks (e.g., manipulate or transfer an object through space) in manufacturing and/or assembly, packing and/or packaging, transport and/or shipping, etc. In executing the tasks, the robots can replicate human actions, thereby replacing or reducing the human involvement that would otherwise be required to perform dangerous or repetitive tasks.
However, despite the technological advancements, robots often lack the sophistication necessary to duplicate human sensitivity and/or adaptability required for executing more complex tasks. For example, robots often lack the granularity of control and flexibility in the executed actions to account for deviations or uncertainties that may result from various real-world factors. Accordingly, there remains a need for improved techniques and systems for controlling and managing various aspects of the robots to complete the tasks despite the various real-world factors.
In the packaging industry, traditional systems use offline packing simulators to predetermine packing sequence/arrangement. The traditional packing simulators process object information (e.g., case shapes/sizes) to generate packing plans. The packing plans can dictate and/or require specific placement locations/poses of the objects at destinations (e.g., pallets, bins, cages, boxes, etc.), predefined sequences for the placement, and/or predetermined motion plans. From the generated packing plans, the traditional packing simulators derive source requirements (e.g., sequences and/or placements for the objects) that match or enable the packing plans. Because the packing plans are developed offline in traditional systems, the plans are independent of actual packing operations/conditions, object arrivals, and/or other system implementations. Accordingly, the overall operation/implementation will require the received packages (e.g., at the starting/pick up location) to follow fixed sequences that matches the predetermined packing plans. As such, traditional systems cannot adapt to deviations in the received packages (e.g., different sequence, location, and/or orientation), unanticipated errors (e.g., collisions and/or lost pieces), real-time packing requirements (e.g., received orders), and/or other real-time factors.
Traditional systems can group and pack objects according to rigid predetermined plans. For example, traditional systems transfer and place objects (e.g., boxes or cases) onto a pallet according to a predetermined motion plan. In doing so, the traditional systems either require all objects at a source location to either have a same dimension/type and/or accessed according to a known sequence. For example, the traditional systems would require the objects to arrive (via, e.g., conveyor) at a pickup location according to a fixed sequence. Also, for example, the traditional systems would require the objects at the pickup location to be placed at designated locations according to a predetermined pose. As such, traditional systems require one or more operations to order or place the objects at the source (i.e., before the packing operation) according to the predetermined sequence/arrangement. Providing the packages in a specific sequence to the robot can be a laborious task for humans. There are some machines, e.g., a shuttle sequencing buffer, that sequence the packages before passing them to the robot for further arrangement. However, these machines can be very expensive, require maintenance, and consume significant resources, such as space and power.